15 citations · 36 across the 4 of their papers we have counts for
4 papers · 1 filter
Interpretable Survival Analysis for Heart Failure Risk Prediction
Mike Van Ness, Tomas Bosschieter, Natasha Din +3
Survival analysis, or time-to-event analysis, is an important and widespread problem in healthcare research. Medical research has traditionally relied on Cox models for survival an…
Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes
Tomas M. Bosschieter, Zifei Xu, Hui Lan +5
Although most pregnancies result in a good outcome, complications are not uncommon and can be associated with serious implications for mothers and babies. Predictive modeling has t…
The Missing Indicator Method: From Low to High Dimensions
Mike Van Ness, Tomas M. Bosschieter, Roberto Halpin-Gregorio +1
Missing data is common in applied data science, particularly for tabular data sets found in healthcare, social sciences, and natural sciences. Most supervised learning methods only…
Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes
Tomas M. Bosschieter, Zifei Xu, Hui Lan +5
Most pregnancies and births result in a good outcome, but complications are not uncommon and when they do occur, they can be associated with serious implications for mothers and ba…